Use artificial intelligence to score your leads by how well they fit your company’s successful conversion patterns. Give your sales team access to scores that help them prioritize leads. Turn on Einstein Lead Scoring, and then decide which conversion milestone to use and if you want Einstein to omit any leads or ignore any fields.
Einstein analyzes your past leads, including any custom fields, to determine which current leads have the most in common with leads that have previously converted. By default, Einstein scores your leads using all lead fields. If your admin is certain a field doesn’t affect lead quality, they can tell Einstein to ignore the field.
Based on its data analysis, Einstein creates a predictive model for your organization. Einstein reanalyzes your lead data every 10 days and refreshes your scores. So if new trends emerge, Einstein won’t miss them.
The lead score appears in the Einstein Score component on lead detail pages. The component also shows sales reps which of the lead’s fields had the greatest influence on its score (1). Depending on the lead, fields with positive or negative influences can appear. Fields that aren’t listed in the Einstein Score component still influence the score, but less than the fields listed.
When you or your users add the Einstein Score field to list views, hovering over a score (1) displays the top factors (2) behind the score. When sales reps focus on leads with higher scores, they’re likely to convert more of them to opportunities. The lock (3) indicates that the score is read-only.
Atul Gupta is CloudVandana’s founder, an 8X Salesforce Certified, working with globally situated businesses in creating Custom Salesforce Solutions.
A strong, dynamic, and accomplished leader, as Director at Atul Gupta, guides all the aspects of CloudVandana Salesforce Implementation Team, Analytics, and Information Technology functions.